Accountable for ensuring the products & services are supported by the right architectures, solutions and data models that meet customer needs
Accountable for ensuring the design of the product solutions and data models are cost effective and maintained through the agile development lifecycle, managing the flow of the backlog of design activities
Working with DevOps Engineers to ensure operational issues (data/solution enhancement, performance, operator intervention, alerting, design defect related issues, etc) are resolved and that any design related issues are addressed in a timely manner
Convert requirements into actionable product/service requirements that feed technology solutions and data modelling development & influence service direction
Accountable for ensuring solutions are aligned with the platform data and solution architecture and roadmap, group standards and policies and the overall enterprise architecture for their function
Work with Architects and DevOps to design and manage best practice on data and solution architecture with clear documentations
Participate in team-wide data and architecture governance activities to review solutions and data models design, manage documentations and artifacts, and review use case implementations to ensure delivery in alignment to solution design and best practices
Define and communicate commercially aware architecture designs for data solutions in accordance to design authority governance
Create clear architecture solution choices, to provide analysis on benefits/challenges
Provide governance on architecture. Design, implement standards and target operating models for IT activities
Development and support of Integration standards, patterns, strategies, roadmaps and architectures
requirements-expected :
University graduate of Computer Science/IT-related field, preferably with major in Software/System engineering or relevant working experience in the field
At least 8 years’ data engineering / architecture; and 5 years’ data modelling
Strong banking industry knowledge
Strong knowledge on data architecture, including data modeling (conceptual / logical / physical data modeling), transactional database solution designing vs analytical database solution designing, ETLs, data-related non-functional requirement design (e.g. backup, archiving, performance etc)
Strong stakeholder management skills to explain technical concepts to numerous business and technical stakeholders in English
Strong analytical skills to assess design effectiveness and limitations and assist design decisions based on facts and business values
Data warehousing experience – Kimball, Inmon approaches for data modeling
Good technical background related to Big Data technologies & open source stack in Big data space:
o Experience with Python/Scala and Spark
o Understanding of HDP ecosystem, HDFS file formats – Avro, Parquet, ORC, Json
o SQL queries optimization on top of Hive (Tez, SparkSQL, MapReduce)
Knowledge of public cloud platforms – Google, AWS, Azure:
o Data processing, Devops experience & experience of data modeling (relational, dimensional)
Good technical background related to Data warehousing in one or more such technologies: Oracle, Teradata, DB2
Metadata management techniques experience
Experience with BI Tools (Tableau, QlikView, QlikSense)
Knowledge of usage Design Tools (EA, Archimate, UML, IDA)
Knowledge of SDLC/Agile methods
Strong interpersonal, communication and presentation with good command of written and spoken English
benefits :
sharing the costs of sports activities
private medical care
sharing the costs of professional training & courses
